Neural network models have been actively applied to word segmentation, especially
Chinese, because of the ability to minimize the effort in feature engineering. Typical …

Most previous approaches to Chinese word segmentation formalize this problem as a
character-based sequence labeling task where only contextual information within fixed sized …

Character-based word segmentation models have been extensively applied to Asian
languages, including Thai, owing to their promising performance. These models estimate …

Neural models with minimal feature engineering have achieved competitive performance
against traditional methods for the task of Chinese word segmentation. However, both …

Character-based and word-based methods are two different solutions for Chinese word
segmentation, the former exploiting sequence labeling models over characters and the latter …

Character-based and word-based methods are two main types of statistical models for
Chinese word segmentation, the former exploiting sequence labeling models over …

Neural word segmentation has attracted more and more research interests for its ability to
alleviate the effort of feature engineering and utilize the external resource by the pre-trained …

Neural network has become the dominant method for Chinese word segmentation. Most
existing models cast the task as sequence labeling, using BiLSTM-CRF for representing the …

Recently, neural network models for natural language processing tasks have been
increasingly focused on for their ability of alleviating the burden of manual feature …

A wide variety of neural-network architectures have been proposed for the task of Chinese
word segmentation. Surprisingly, we find that a bidirectional LSTM model, when combined …